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Abstract Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (N P1 = 10, N P2 = 17, N P3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

Climate data (e.g., air temperature and precipitation) are used in a variety of environmental sectors, such as determining which crops to cultivate for farmlands, and optimizing the placement of products in retail stores. Currently, access to climate data is popularly managed by sophisticated database management systems, which are efficient for data processing but have limited data exchangeability across systems. By contrast, many semantic studies focus on the use of RDF knowledge graphs (KGs) for climate data access, which is semantically interoperable such that data can be easily shared between different RDF KGs based on predefined domain ontologies. However, climate data is often consumed in a certain geographical context to understand its relationships with other environmental sectors. For example, the geographical context of farmyards is needed to determine which climate stations nearby are used. The interoperability proposed for the geographical context of climate data access is under-explored by relevant semantic studies, resulting in additional resource waste in integrating heterogeneous geospatial information for climate data access. In this article, we propose LinkedGeoClimate, which is an interoperable RDF KGs platform for climate data access within an enriched geographical context. LinkedGeoClimate provides the necessary geographical and geospatial information for climate data access and further advances interoperable climate data access when mutual spatial relationships with other environmental sectors are concerned.

The rapid proliferation of environmental sensor networks (ESNs) used for monitoring environmental systems, such as meteorology and air quality, and advances in database technologies [e.g., structured query language (SQL)] has made significant progress in sensor data management. Notwithstanding the strength of these databases, they can inevitably lead to a data heterogeneity problem as a result of isolated databases with distinct data schema, which are expensive to be accessed and preprocessed when the data are consumed spanning multiple databases. Recently, knowledge graphs have been used as one of the most popular integration frameworks to address this data heterogeneity problem from the perspective of establishing an interoperable semantic schema (also known as ontology). However, the majority of the proposed knowledge graphs in this domain are a product of an extraction–transform–load approach with all the data physically stored in a triplestore. In contrast, this article examines an approach of virtualizing knowledge graphs on top of the SQL databases as the means to provide a federated data integration approach for enhanced heterogeneous ESNs' data access, bringing with it the promise of more cost efficiency in terms of input/output, storage, etc. In addition, this work also considers some motivating application scenarios regarding the efficiency of time-series data access. Based on a performance comparison between the proposed integration approach and some popular triplestores, the proposed approach has a significant edge over triplestores in multiple time-series structuring and acquisition.

Climate change has been deemed to be one of the greatest challenges facing humans in the 21st century, with extreme weather events taking place more regularly than before. While the impact of climate change has been well documented in recent years across industries, the impact of climate change on the tourism economy is yet to be fully realized. This article aims to apply a range of knowledge graph techniques to naturalistic data. Among these, weather data will be explored as one prospective way to enhance people's understanding of how climate and a country's tourism economy are related and how they interact. According to our exploration with the knowledge graph approach in organizing the climate and tourism data, the insights and knowledge gained from the knowledge graph are able to ultimately help improve the quality of life for people and the tourism industry of a country.

Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as CSV, JSON, and XML. To address the data isolation problem, several semantically uplifted knowledge graphs are proposed for climate data exchange. While these knowledge graphs improve data interoperability, the advancement in multisource data interchange is limited to data included inside knowledge graphs. As a result, the exclusive interoperability of current climatic knowledge graphs hampers the flow of data into typical climate analysis workflows in contexts, where analytical models often need data in nonknowledge graph formats. This article addresses this issue by focusing on enhancing climate analysis workflows within the context of the Python machine learning environment, with a preference for tabular data. We propose an analysis workflow able to automatically integrate remote climate knowledge graph data with local tabular data so as to enhance the data usability with respect to certain climate analysis tasks. To underscore the importance of our study, we illustrate how the workflow streamlines the access to multisource climatic variables in the Python environment from a semantic perspective. The additional knowledge graph data have the potential to augment local datasets in the climate domain, as evidenced by an improvement in accuracy of up to 10% for machine learning geared on rainfall detection.

Background: The benefits of having high-quality healthcare data are well established. However, high-dimensionality and irregularity of healthcare data pose challenges in their management. Knowledge graphs have gained increasing popularity in many domains, as a method for representing data to overcome such challenges. One important factor in representing data is “time”. Data with time related attributes are considered, temporal data. Temporal data are frequently observed in healthcare and the management of rapidly changing patient data is an ongoing challenge. Traditionally, data models have focused on presenting static data and do not account for temporal data. Temporal data models ensure time consistency in data models and assist analysing the history of data and predicting the future trends in data. Knowledge graphs can include temporal data models and are therefore of interest to the field of healthcare data management. As such, the herein aim is to outline a protocol for an inter-disciplinary systematic review of approaches, applications and challenges in modelling temporal data in knowledge graphs so that we can inform the application of knowledge graphs to healthcare data. Method: The research questions is, what are the existing approaches in modelling temporal data in RDF based knowledge graphs. Two sub-questions on applications, and challenges will also be evaluated. ACM digital library, IEEE Xplore and Scopus will be searched for this review. The search will be limited to peer-reviewed literature referring to knowledge graphs based on Resource Description Framework (RDF). A narrative synthesis of the papers will be conducted. Conclusion: The findings of this systematic review will be useful for data engineers to better represent data and perform analytics through temporal data. They can be applied in the context of healthcare data and the current challenges faced in managing rapidly changing patient data.